P19 EXTERNAL VALIDATION OF THREE EXISTING EARLY MYELOMA RELAPSE SCORES (BY EBMT, CIBMTR, AND GIMEMA) IN A SINGLE CENTER SHOWS MAJOR DIFFERENCES
Authors: M Beksac, G Cengiz Seval, S Civriz Bozdag, SK Toprak, M Kurt Yuksel, P Topcuoglu, O Arslan, M Ozcan, T Demirer, O Ilhan, G Gurman
Journal: HemaSphere (2023)

Introduction: Despite progress in induction regimens, early relapse of multiple myeloma (MM) following autologous hematopoietic cell transplantation (AHCT) is still a frequent complication that confers poor prognosis. Recently, three groups (CIBMTR, GIMEMA (S-ERMM), and EBMT) have published scoring systems to predict early relapse. In this retrospective analysis, we aimed to validate and compare these novel scores in a homogenous population treated in a single center. Methods: This retrospective study included 410 consecutive newly diagnosed myeloma patients treated in Ankara University, all of whom received 3-8 cycles (median 4) of mostly (69.5%) bortezomib based doublet or triplet (VCD) regimens prior to AHCT with Mel200 from October 2005 to January 2021, with a minimum follow-up of ≥12 months after ASCT. Consolidation was administered until CR; maintenance was not standard unless high-risk disease was detected at diagnosis. No tandem transplants were included. Results: The median age of all patients in the analysis was 62 years (36-83 years), and female/male:43.4%/56.6%. In our cohort, 60 (19.5%) patients received consolidation while maintenance treatment was given to 108 (26.3%) patients. The median follow-up after AHCT for all patients is 38.4 m (12-156 m) and 82 patients (20%) have relapsed within 12 months. The characteristics and distribution of the patients within scoring groups are shown in Table. Neither pre- nor post-AHCT response alone or FISH findings were associated with early relapse. With EBMT scoring (which encompasses ISS, response, and performance status at the time of AHCT), early relapse was observed among Score 0 (1.4%) and 1(12.6%), CIBMTR low (13.4%) and S-ERMM low-risk (14.4%) scores showing the EBMT score 0 to recognize very low risk better than CIBMTR and GIMEMA models. Likewise, EBMT score 3(36.3%) and 4(56.3%) compared to CIBMTR high (17.6%), S-ERMM high (43.7%) point to EBMT score 5 to recognize early relapse risk the best. Similarly, “no relapse risk” distribution across risk groups points to EBMT score 0, to recognize very good patients best compared to “low risk” with CIBMTR or S-ERMM scores. The Hazard Risk for early relapse was also the highest and most significant within EBMT scores: 3.4 (95% CI, 1.9-6.6; S-ERMM high vs low; p<0.001), 5.6 (95% CI, 3.1-10.4; EBMT score 3-4 vs. 0-1; p<0.001), and 1.3 (95% CI, 0.4-4.5; CIBMTR high vs low p=NS). The impact of scores on PFS are shown in Figure-1. Conclusion: Efforts are ongoing on prediction models of early relapse. S-ERMM and CIBMTR scores are either limited to specific populations based on induction/conditioning or parameters not applicable to daily practice worldwide. EBMT score presented at ASH 2021 (Beksac et al.) differs from earlier scores by being based on a large real-world frequently used triplet induction and conditioning regimen dataset. In this analysis, we attempted to validate the EBMT, CIBMTR, and S-ERMM scoring models in an external cohort, demonstrating that the EBMT is a reliable tool that recognizes very low and very high-risk patients at the time of AHCT prior to the onset of clinical relapse. In the future, with the introduction of molecular features, this prototype model may be improved.